Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data

In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Itô semimartingales, and discuss how to measure liquidity risk using high-frequency financial data. In particular, we investigate the impact of non-stationary microstructure nois...

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Veröffentlicht in:Journal of econometrics 2017-09, Vol.200 (1), p.79-103
Hauptverfasser: Chen, Richard Y., Mykland, Per A.
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description In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Itô semimartingales, and discuss how to measure liquidity risk using high-frequency financial data. In particular, we investigate the impact of non-stationary microstructure noise on some volatility estimators, and design three complementary tests by exploiting edge effects, information aggregation of local estimates and high-frequency asymptotic approximation. The asymptotic distributions of these tests are available under both stationary and non-stationary assumptions, thereby enable us to conservatively control type-I errors and meanwhile ensure the proposed tests enjoy the asymptotically optimal statistical power. Besides, it also enables us to empirically measure aggregate liquidity risks by these test statistics. As byproducts, functional dependence and endogenous microstructure noise are briefly discussed. Simulation with a realistic configuration corroborates our theoretical results, and our empirical study indicates the prevalence of non-stationary microstructure noise in New York Stock Exchange.
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subjects Asymptotic methods
Endogenous
High frequency trading
High-frequency tests
Liquidity
Microstructure
Noise
Non-stationarity
Risk
Simulation
Stable central limit theorems
Statistical power
Statistical powers
Stock exchanges
Studies
Volatility
title Model-free approaches to discern non-stationary microstructure noise and time-varying liquidity in high-frequency data
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